NISTIR 7913

Review on Sustainability 

Characterization for Manufacturing 

Processes

Mahesh Mani

Jatinder Madan

Jae Hyun Lee

Kevin W. Lyons

Satyandra K. Gupta

http://dx.doi.org/10.6028/NIST.IR.7913
NISTIR 7913

Review on Sustainability 

Characterization for Manufacturing 

Processes

Mahesh Mani

Jatinder Madan

Jae Hyun Lee

Kevin W. Lyons

Systems Integration Division

Engineering Laboratory

Satyandra K. Gupta

Department of Mechanical Engineering

University of Maryland 

http://dx.doi.org/10.6028/NIST.IR.7913

February 2013

U.S. Department of Commerce 

Rebecca Blank, Acting Secretary

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director
ii

Table of Contents

1. Introduction.............................................................................................................................. 1

2. Manufacturing Performance Assessment ................................................................................ 2

2.1. Process Characterization vs. Performance Measurement................................................. 2

2.2. Challenges in Developing the Measurement Science....................................................... 3

3. Background Review................................................................................................................. 4

3.1. Manufacturing Process Taxanomy ................................................................................... 4

3.1.1. Unit manufacturing process research committee report ............................................ 4

3.1.2. Todd et al. manufacturing taxonomy......................................................................... 5

3.1.3. DIN 8580 classification ............................................................................................. 5

3.1.4. Ashby’s taxonomy ..................................................................................................... 6

3.1.5. Manufacturing management taxonomy ..................................................................... 7

3.1.6. Industry level classifications...................................................................................... 7

3.2. Indicators for Sustainable Manufacturing......................................................................... 7

3.2.1. Earlier efforts................................................................................................................ 7

3.2.2. Key sustainable manufacturing indicators.................................................................... 8

3.3. Manufacturing Process Models ...................................................................................... 10

3.3.1. Classification of process models.............................................................................. 10

3.3.2. Process modeling languages.................................................................................... 11

3.3.3. Manufacturing process – activity models................................................................ 12

3.3.4. Manufacturing process - information models.......................................................... 13

3.4. Software Tools................................................................................................................ 15

4. Methodology Development ................................................................................................... 16

4.1. Sustainability Characterization Methodology ................................................................ 17

5. Conclusions............................................................................................................................ 19

6. Disclaimer.............................................................................................................................. 19

7. Bibliography ............................................................................................................................. 19
1

Abstract: Manufacturing industries lack the measurement science and the needed information base to 

measure and effectively compare environmental performances of manufacturing processes, resources and 

associated services with respect to sustainability. The current use of ad-hoc methods and tools to assess 

and describe sustainability of manufactured products does not necessarily account for manufacturing 

processes explicitly, and hence results in inaccurate and ambiguous comparisons. Further, we identified 

that there are no formal methods for acquiring and exchanging information that help establish a 

consolidated sustainability information base. Our ultimate goal is to develop the needed measurement 

science and methodology to evaluate sustainability of fundamental manufacturing processes to ensure 

reliable and consistent comparisons. As a precursor, this report presents a background review on related 

work to evaluate sustainability performance. We discuss identified manufacturing process classifications, 

sustainable manufacturing indicators and computable metrics, relevant information models, and software 

tools. An overview of future research is also presented. 

Keywords: sustainability characterization, performance measurement, unit manufacturing processes, key 

performance indicators, measurement science, knowledge management

1. Introduction

Sustainable manufacturing is defined as the creation of manufactured products that use processes that 

minimize negative environmental impacts, conserve energy and natural resources, are safe for employees, 

communities, and consumers and are economically sound [1]. According to the National Association of 

Manufacturers, the industrial sector accounts for 31 % of all the energy consumed in the United States. 

Manufacturing alone accounts for 65 % of the industrial sector’s energy consumption [2]. With 

manufacturers looking to diversify their energy supplies and improve their energy efficiency due to an 

increasing energy price tag, a model for sustainable manufacturing among industries has become 

important.

Manufacturing processes and their corresponding resources are developed to support the 

production of end-user products. Ultimately, the focus is on meeting customer needs, besides being 

sustainable. Further, to enforce compliance, it is expected that manufacturing industries today pay 

emission taxes [3]. Sustainability related performance measurement of manufacturing processes has hence 

become a crucial effort.

Performance measurement, in general, identifies the gaps between the current and desired 

performance, and provides an indication of the progress made towards closing the gaps. Related 

performance indicators compress large amounts of information into a format that is easier to understand, 

compare and manipulate. Companies often use such indicators to set targets and monitor their consequent 

success. Traditionally, manufacturing related performance indicators provided information on the 

productivity and included throughput, cost, quality, material, etc. 

Performance measurement of sustainable manufacturing should not only include a performance 

indicator but the metric for that indicator also. The challenge, however, is in identifying the sustainability 

performance indicators that directly relate to manufacturing metrics, e.g. energy utilization versus 

productivity. A number of indicators have been proposed in the past for sustainability performance 

measurement [4]. From this exhaustive list of sustainability performance indicators, some are commonly 

used in the industry, and are also known as key performance indicators (KPIs). These KPIs are used to 

evaluate the success of a particular activity with respect to sustainability [5]. For instance, one of the 

commonly used performance indicator for injection molding process is energy per unit of mass and the 

corresponding metric is kWh per kg (or MJ per kg) of injection molded parts. 

To remain globally competitive, manufacturers must increase the flexibility, speed of production 

systems and their supplier networks, while also reducing environmental impacts and energy requirements

[6]. These changes require a transformation from manufacturing practices based on experience and best 

practices towards science-based modeling, decision making, and production. This review report presents 
2

a state of the art study on manufacturing process characterization, fundamental measurements, standards, 

and tools to enable U.S. manufacturers to make this transformation. The motivation of this report is to 

facilitate the development of the measurement science and methodologies, performance indicators and 

metrics and, related information models for fundamental manufacturing processes to evaluate 

sustainability. 

The report is organized as follows. Section 2 presents how process characterization is an inherent 

part of performance measurement. This section also outlines the challenges in developing the required 

measurement science for sustainability of manufacturing processes. Section 3 presents a background 

review on the different manufacturing process classifications, sustainability indicators, process 

information models followed by a brief review of the relevant software tools for sustainability. Section 4 

presents the methodology development for sustainability characterization for manufacturing processes. 

Section 5 presents our proposed scope of work towards sustainability characterization followed by 

conclusions. 

2. Manufacturing Performance Assessment

In this section, we first outline how process characterization is an inherent part of performance 

measurement and discuss how process characterization can be used for developing the science-based 

performance measurement for sustainability. Next, we briefly present the challenges in developing the 

required measurement science followed by the relevant research efforts made towards development of 

methodologies for performance measurement for sustainability. Finally, we conclude the section 

summarizing the benefits associated with the development of science based performance measurement 

methodologies for sustainable manufacturing. 

2.1. Process Characterization vs. Performance Measurement

A process characterization activity typically identifies key inputs and outputs of a process, collects data 

over the entire operating range, estimates the steady-state behavior at optimal operating conditions and 

builds models describing the parametric relationships across the operating range. The result of process 

characterization activity is a set of mathematical process models that can be used to monitor and improve 

the process [7]. Figure 1 [8] shows an instance of a process as a system boundary along with possible 

inputs and outputs.

Manufacturing process characterization can be useful when: bringing a new process or tool into 

use, bringing a tool or process back up after scheduled/unscheduled maintenance, compare tools or 

processes, check the health of our process during the monitoring phase, troubleshooting a bad process, or 

in our case, to determine the sustainability performance.

Performance measurement is complementary to process characterization, with feedback control 

for improved results. We are of the opinion that production process characterization [7] can be used as a 

promising methodology for sustainable manufacturing; the challenge however is in developing the 

measurement science.
3

Figure 1 Boundaries of a process under study 

2.2. Challenges in Developing the Measurement Science

The major obstacle in developing absolute measures for sustainability is the absence of a well-defined 

approach to characterize sustainability for manufacturing. Characterizing sustainability will help 

manufacturing industries to improve productivity through ways of improvement in process control by 

way of better resource management. Different manufacturing processes and resources have different or 

overlapping capabilities with varying efficiencies. It is well understood that the currently available LCA 

(Life Cycle Assessment) tools [9,10] use LCI (Life Cycle Inventory) databases, which are typically 

limited to primary material production only (e.g. sheets, foils, etc.) and recycling processes [11]. General 

approximations made today for sustainability ignores the manufacturing process related LCI and hence 

result in inaccurate planning for cross comparisons and decision making.

Developing the measurement science for sustainability at various system levels (e.g. resources vs. 

facility, facility vs. factory, and factory vs. supply chain) can help manufacturing industries for enhanced 

manufacturing resource management. 

Following are the challenges to develop the measurement science:

• uncertainties in manufacturing environment

• dramatic changes in customer requirements

• innovation in production technology

• uncertainties in internal operating environment

• inadequate traditional approach to overcome uncertainties 

• inadequate and unstructured information 

• inadequate decision models

• undefined scope and boundaries within manufacturing unit processes

• multiple unverified proposed measurement methodologies 
4

Note that sustainability, like quality, is in the eyes of the beholder. The meaning of sustainability 

varies, depending on the sector, category and region. Sustainability performance measurement for a 

manufacturing process can be used as a means of representing an aspect of quality. One analogy is the 

way impact factor of journal citations has become an accepted standard of quality. For sustainable 

manufacturing, a similar metric that delivers the impact and caters to an unbiased comparison is lacking. 

We propose to develop a science-based performance measurement for sustainable manufacturing building 

upon the traditional production process characterization methods [7]. 

3. Background Review 

In this section we report and discuss on the related work towards developing the measurement science for 

sustainable manufacturing. We first review relevant classifications or taxonomiesi of manufacturing 

processes to understand their similarities and differences. Next, we briefly discuss the sustainability 

indicators with an emphasis on the key sustainability indicators for manufacturing. Then, we provide an 

overview of the relevant manufacturing process information models in the context of sustainable 

manufacturing. Finally in this section, we present selected software tools that aid in computing 

sustainability and their shortcomings.

3.1. Manufacturing Process Taxonomy

Manufacturing processes involve the conversion of raw materials into finished products with specific 

shape, structure, and properties to fulfill certain requirements [8]. This conversion into finished products 

is accomplished using a variety of manufacturing processes that utilize energy to produce controlled 

changes in the configuration properties of materials. The energy applied during processing may be 

mechanical, thermal, electrical, or chemical in nature. The results are meant to satisfy functional 

requirements that were defined during the product design stage. With growing concerns towards 

sustainability, and sustainable manufacturing in particular, these individual manufacturing processes must 

be meticulously characterized for performance efficiency. A manufacturing process taxonomy, i.e., welldefined manufacturing process classification, helps one to understand the different processes to identify 

ways to manufacture different products, understand associated process accuracy, competitive and relative 

process capabilities and most importantly get all of the right information to make a decision. The 

classification should also provide the depth of information depending on the choice of manufacturing 

process. The following are selected taxonomy/classifications of manufacturing processes available in the 

literature.

3.1.1. Unit manufacturing process research committee report

The Unit Manufacturing Process Research Committee, Commission on Engineering and Technical 

Systems, National Research Council (NRC) identifies unit processes as the individual steps required to 

produce finished goods by transforming raw material and adding value to the work piece as it becomes a 

finished product [8]. The information and material flow associated with a typical unit process is shown 

in Figure 2. Raw material or parts from a previous unit process are inputs. The output consists of parts, 

which are one step closer to their final form, and of an influence on the environment, such as particulate 

or noise pollution. The information input and control to the unit process include product data, process 

information, and process control methodology. The resource requirements of the unit process are such 

items as manufacturing equipment, energy, and human resources.
5

Figure 2 Unit process information and input materials flow representation

The NRC report [8] considers a unit process to be optimized when the value added in terms of the 

required configuration and property changes are delivered to the work-piece in the most cost-effective 

manner from the system as a whole. This involves minimization of factors such as energy use, scrap 

generation, labor costs, and capital equipment requirements. According to NRC, unit manufacturing 

processes are classified into five families of physical processes:

• Mass-change processes involve material removed or added by mechanical, electrical, or chemical 

means. These include plating, machining, grinding, as well as nontraditional removal processes 

such as electro-discharge and electro-chemical machining.

• Phase-change processes involve producing a solid part from material originally in the liquid or 

vapor phase. These include casting of metals, infiltration of composites, and injection molding of 

polymers.

• Structure-change processes involve altering the microstructure of a work-piece, either throughout 

its bulk or in a localized area, such as its surface. These include heat treatment and surface 

hardening processes. 

• Deformation processes involve altering the shape of a solid work-piece without changing its mass 

or composition. These include processes of rolling and forging, and sheet-forming processes of 

deep drawing and ironing.

• Consolidation processes involve combining materials such as particles, filaments, or solid 

sections to form a part or component. These involve powder metallurgy, ceramic molding, and 

polymer-matrix composite pressing. Joining processes, such as welding and brazing, also belong 

to this process family.

3.1.2. Todd et al. manufacturing taxonomy

Six families of shaping processes that change the basic geometry of shape of a work piece (mass 

reducing, thermal mass reducing, chemical mass reducing, mass conserving, consolidation, and joining) 

and four families of non-shaping processes that modify the engineering and aesthetic properties of 

materials (hardening, softening, surface preparation, and surface coatings) are identified. This 

classification now used in the United States and Europe, is a valuable tool in identifying processes and 

their capabilities. Using the same classification system, detailed descriptions of each individual process 

can be found in the Manufacturing Processes Reference [12].

3.1.3. DIN 8580 classification

German standard, DIN 8580 [13] applies to a broad area of the manufacturing processes. It defines and 

explains basic concepts needed for the description and classification of manufacturing processes. 

Furthermore, in this standard the foundations for building a classification system are established. It 

classifies manufacturing processes in groups based on the criteria of: created from an initial form (original 

form), created from formless substance, the change in shape and material properties etc. Classification 

Product Data

Process Data

Product

Effect on Environment

Resources:

Equipment

Workforce

Energy

Input Material 

or Parts

Unit Process
6

results in six main groups of the manufacturing processes as shown in Table 1. This standard is being 

used in the international cooperative effort on process emissions in manufacturing [14]. 

Table 1 Scope of the relevant manufacturing process classifications

Source

NRC Todd et al DIN 8580 Paul DeGarmo Ashby Mil

Classification

Five families of 

physical 

processes 

• Mass-change 

processes

• Phase-change 

processes

• Structurechange 

processes

• Deformation 

processes

• Consolidation 

processes

Six families of 

Shaping 

processes 

• Mass 

Reducing

• Thermal Mass 

Reducing

• Chemical 

Mass 

Reducing

• Mass 

Conserving

• Consolidation

• Joining

Four families of 

Non-shaping 

processes 

• Hardening

• Softening

• Surface 

Preparation

• Surface 

Coatings

Six groups of 

manufacturing 

processes 

• Original 

Forming

• Transforming

• Separating

• Joining

• Coating and 

Finishing 

• Change of 

Material 

Properties

Seven types are 

identified

• Casting or 

Molding

• Forming or 

Shearing

• Machining

(material 

removal)

• Heat Treating 

• Finishing

• Assembly 

• Inspection

Four groups of 

manufacturing 

processes

• Primary 

shaping 

processes: 

• Secondary 

processes: 

• Joining

• Finishing

Manufacturing 

Management 

Taxonomy

• Product 

Design

• Process 

Design& 

Control

• Supply Chain 

Management

Basis of 

classification

Physical change Shaping/NonshapingForming/ 

Transforming/ 

Material 

Property

Casting/Forming/ 

Material Property

Primary/ 

Secondary

Manufacturing 

Management

Advantages

Science-based Ease of use Being used in 

CO2PE! effort 

for determining 

LCI of 

Manufacturing 

Processes

Simple Simple Management

3.1.4. Ashby’s taxonomy

Based on a survey of all standard processes (and not-so-standard) for materials manufacturing, Ashby 

proposes [15] four categories of manufacturing processes: 

• Primary shaping processes: These include processes like casting, deformation (forging and 

rolling), powder processing (ceramics, nano-structured materials), and special methods like 

electro-forming and rapid prototyping.

• Secondary processes: Includes machining and heat treatment. They are secondary because they 

are used on something that’s already been through some primary process.

• Joining: Includes welding, adhesive bonding, and fasteners. 

• Finishing: Many of these are cosmetic, but also include performance improvement processes like 

polishing, coatings, etc.
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3.1.5. Manufacturing management taxonomy

In the Acquisition Community Connection [16], a manufacturing management taxonomy provides a flow 

down to identify, develop, capture and present various manufacturing knowledge objects within the 

manufacturing management special interest area [16]. Figure A1 in the Appendix presents the 

representative flow down.

3.1.6. Industry level classifications

Industry classifications based on the service are also available. For example in the mfg.com, the 

classification is based on supplier selection for manufacturing and outsourcing [17]. 

A background review of available manufacturing taxonomies enabled us to find pros and cons associated 

with each. Some classifications were at a high level and needed further breakdowns like the one defined 

by NRC. Todd & Allen’s taxonomy is a textbook popular classification, while DIN 8580 is a popular

German classification. Some other classifications were catered more towards manufacturing 

management. However, the closest and most extensive were Todd et al.’s and the DIN 8580 taxonomy.

Present international efforts for manufacturing process life cycle inventory primarily use the DIN 8580

[14] taxonomy, although co-operating institutions can also opt for Todd and Allen’s taxonomy [12]. To 

achieve consistency with similar efforts, we plan to use Todd et al.’s and DIN 8580 taxonomy for 

sustainability process characterization purposes. 

3.2. Indicators for Sustainable Manufacturing 

There are a number of indicators for sustainability, which include indicators based on environmental 

stewardship, economic growth, social well-being, technological advancement and performance 

management [18]. Researchers have been working to define and use indicators for sustainable 

manufacturing. Such research efforts to use indicators for sustainable manufacturing are maturing fast. In 

this section, we first discuss the earlier efforts, followed by a discussion on the presently acceptable and 

better recognized indicators for sustainable manufacturing. 

3.2.1. Earlier efforts 

Fan et al. [19] suggested that a comprehensive system is needed to implement sustainability indicators in 

manufacturing companies. They shortlisted a number of indicators used for sustainability measurement 

and attempted to evaluate those based on the criteria of relevance, analytical soundness and measurability. 

Shaw and Joung [20] proposed a sustainable manufacturing measurement infrastructure. They define 

sustainability measurement process as a sequence of operations, with the necessary instruments and tools 

and having the objective of determining the value of an indicator. Laurent et al. [21] investigated the 

ability of CO2 emissions (or carbon footprint) as a performance indicator for product or production 

activities. Their focus was to find out if there is any correlation between the Carbon Foot Print (CFP) and 

other types of environmental impacts like Human Toxicity Impacts (HTI). The overall environmental 

impact was investigated based on life-cycle assessment of several materials of major importance to 

manufacturing viz. metals and plastics. 

In addition to the earlier efforts, there have been efforts by multiple organizations to define and 

adopt sustainable manufacturing indicators. An effort by the National Institute of Standards and 

Technology (NIST) is the Sustainable Manufacturing Indicator Repository (SMIR) [22], which defines

indicators for sustainable manufacturing and addresses a range of sustainability issues. From an extensive 

review of publicly available indicator sets, the SMIR is based on five dimensions of sustainability: 

environmental stewardship, economic growth, social well-being, technological advancement, and 

performance management. 
8

Most recently, the Organization of Economic Co-operation and Development (OECD) has 

published a Sustainable Manufacturing Toolkit [23] which defines 18 indicators for sustainable 

manufacturing in three categories; namely inputs, operations and products. The input indicators are nonrenewable material intensity, restricted substances material intensity, and recycled-reused contents. The 

indicators for operations, eight in number, are water intensity, energy intensity, renewable proportion of 

energy, greenhouse gas intensity, residual gas, air release intensity, water release intensity, and proportion 

of natural land. Besides these, there are seven indicators for products, namely recycled/reused content, 

recyclability, renewable material content, non-renewable material intensity, restricted substances content, 

energy computation and greenhouse gas emissions intensity. 

We identified that some indicators do affect performance of the other indicators. For example, the 

energy intensity effects other indicators like greenhouse gas intensity or renewable proportion of energy.

In the following section, we discuss key indicators, which are important in the sense that these can help to 

improve manufacturing from the sustainability point of view. Manufacturing industries use these 

indicators when reporting sustainability [24]. 

3.2.2. Key sustainable manufacturing indicators

a. Energy indicator

Conserving and using energy optimally is crucial in manufacturing. Within the facility, energy 

conservation can be improved by monitoring the manufacturing process and corresponding equipment

where losses occur due to escaped heat, standby mode, etc. We use the Organization for Economic Cooperation and Development [4] indicators for demonstration purposes.

According to OECD, any energy production, whether non-renewable or renewable, depletes nonrenewable resources (including habitats, fossil fuels and uranium), generates GHG (greenhouse gases) or 

both. Energy intensity (EI) in Mega Joule (MJ) is calculated by using Eq. 1 for production processes and 

overheads. The OECD suggests the energy intensity of the inputs can be included by extending the

accounting boundary.

EI = (Energy consumed in production processes+Energy consumend in overhead)

Normalization factor (1)

Depending on the production process, energy computations will have to be made. Also,

depending on the production process there may be multiple methods to calculate energy consumed. For 

example, if we consider material removal by machining processes, energy can be computed based on the 

material removed during the process (removed volume) and the specific energy of the material. The 

amount of energy can also be computed for a direct energy meter reading. We must note here that there 

will be uncertainties and heuristics associated with the type of energy computation methodology and 

hence it is important to consider this when we characterize manufacturing processes to handle such 

uncertainties. We discuss sustainability characterization methodology in Section 4. 

b. Carbon dioxide

Carbon dioxide emission calculations are dependent on the electricity generation based on the figures 

from the EPA’s eGRID emission factors. According to the EPA, on an average, electricity sources emit 

0.5925 kg of CO2 per kWh. CO2 emissions for a particular state per kWh may vary greatly in accordance 

with the amount of clean energy in the energy supply. For example for Maryland, CO2 emissions are 

0.608 kg/kWh (1,337.6 lb/MWh) while for the District of Colombia it is 1.2644 kg/kWh (2,781.7 

lb/MWh) [25]. This indicator represents the GHG intensity of the facility including production processes,

and overhead (energy-related emissions and business travel). The GHG (greenhouse gas) emissions 

associated with the production of input materials and the logistics (shipping of inputs and finished 

products as well as staff commuting) can also be counted by extending the accounting boundary, and has 
9

been included in Eq. 2. The Organization of Economic Co-operation and Development (OECD) states 

that the GHG intensity of a product use stage must be calculated separately and is not included here.

GHG Intensity =

�

GHGs released in energy consumption for production+

GHGs released in energy consumption for overhead+

GHGs released by transport used for business travel+

Additional GHGs released from production process �

Normalization factor (2)

Unit of the indicator: tCO2equivalent/normalization factor

c. Waste

Waste minimization involves efforts to minimize resource and energy use during manufacture. For the 

same commercial output, usually the fewer materials are used, the less waste is produced. Waste 

minimization usually requires knowledge of the production process, cradle-to-grave analysis (the tracking 

of materials from their extraction to their return to earth) and detailed knowledge of the composition of 

the waste.

d. Water

Depending on the production process, water is often consumed for cooling, heating or washing. When 

water is consumed, such as when it is incorporated into beverages, it cannot easily be substituted or 

reduced. For that reason, the OECD indicator calculates only the intensity of total water intake of the 

overhead and production process. Equation 3 is used for determining water intensity.

WI = (Total water intake)

Normalization factor (3)

e. Emissions 

It is important for the facility to also track releases of its individual air pollutants of concern, e.g., NOx, 

SOx, persistent organic pollutants (POPs), volatile organic compounds (VOCs), hazardous air pollutants, 

particulate matter, and/or other pollutants that are priorities for the state, region, locality, and public 

interest groups. These may be pollutants that are regulated or permitted. The facility should be able to 

identify which air pollutants to prioritize, and confirm this list with its community, regional or state 

officials.

The emission indicator represents the intensity of the weight of all releases to air during the 

reference year. Although it might be difficult or insignificant to track, the OECD recommends tracking

the releases to air from overhead as well as production processes. Equation 4 provides a mathematical 

definition for air emissions.

Intensity of pollutant releases to air = (Weight of releases to air)

Normalisation factor (4)

Units are ton/normalization factor. 

Table 2 presents a sample sustainability report. Depending on the industry and the sector, manufacturing 

key performance indicators are to be reported. The overall problem is the underlying measurement 

science and procedures in place to measure and report sustainability. Manufacturing industries lack the 

measurement science and the needed information base to measure and effectively compare environmental 

performance of manufacturing processes, resources and associated services with respect to sustainability. 

Moreover, there are no formal methods for acquiring and exchanging information for sustainability in 

manufacturing. The next section presents a review of the manufacturing process models. 
10

Table 2 Sample Sustainability Report

SUSTAINABILITY REPORT Year

Absolute Emissions FY09 FY10 FY11 FY12

Total energy usage (MWh)

 Total gas & oil (MWh)

Total electricity (MWh)

Total CO2 (ton)

Total water usage (1000 m3)

 Total discharged water (1000 m3)

Total VOC emissions (ton)

Total waste-at-cost (ton)

Total waste-to-landfill (ton)

Total European production volumes 

(vehicles)

Number of plants covered by result

3.3.Manufacturing Process Models

To understand the sustainability performance of manufacturing processes, engineers will need welldefined manufacturing process models. The sustainability performance is broadly dependent on all 

information related to manufacturing processes namely resource, tooling, material, and energy. A

manufacturing process model must define relationships between sustainability performance and 

information related to manufacturing processes. Previous manufacturing process models mainly focused 

on what information is related to manufacturing processes yet do not explicitly show how manufacturing 

process information is related to the sustainability performance. 

Subsequent sections provide an analysis and discussion of the previous manufacturing process 

models to identify the needed relationships between manufacturing process information and sustainability 

performance. 

3.3.1. Classification of process models

Process models can be classified into language level and the model level according to information 

abstraction levels. Examples of general modeling languages include XML (Extensible Markup 

Language), UML (Unified Modeling Language), EXPRESS, KIF (Knowledge Interchange Format) and 

OWL (Ontology Web Language). At the language level, a process model defines fundamental entities and 

their relationship. IDEF0 (Integration Definition for Function Modeling) [26], BPMN (Business Process 

Modeling Notation) [27], and PSL (Process Specification Language) [28] are examples of process models 

at the language level. Engineers can use process modeling languages to build process models. 

Process models in the model level can either be activity models or information models. Activity 

models describe data flow and precedence of manufacturing processes. The Systems Integration for 

Manufacturing Applications (SIMA) reference architecture – part 1 [29] defines reference activity models 

for product development. The SIMA activity model comprehensively explains processes and data flow

(i.e. inputs, outputs, reference, and control flows) in product development using the IDEF0. Information 

models of manufacturing processes define entities and their relationships. The data flow in activity 

models can be entities in information models. 

Information models of manufacturing processes can be further classified into the class level, and 

the property level. Information models in the class level focus on classes and their relationships to 
11

represent information related to manufacturing processes. A class diagram in the core manufacturing 

simulation data (CMSD) [30] can be one of information models in the class level. Information models in 

the property level define mathematical models of properties of classes related to manufacturing processes. 

Thiriez [31] analyzed the injection modeling process and provided an information model of the injection 

modeling process. The information model shows how properties of material, mold, machine, and process 

contribute to total energy use in an injection modeling process. The CO2PE! project [14] is also 

developing manufacturing process models at the property level to provide reference models of life cycle 

inventory (LCI) data of manufacturing processes.

Table 3 shows the process model classification. Following sub-sections specifically provide an

analysis of process modeling languages, process activity models, and process information models.

Table 3 Process model classification

Modeling 

languages

General modeling 

languages

XML, 

UML, EXPRESS, 

KIF, OWL

Process-specific 

modeling languages

IDEF0, 

BPMN, 

PSL

Manufacturing

process models

Activity models SIMA reference architecture – Part1

Information models

(Class level)

Core Manufacturing Simulation Data (CMSD)

ISO 15531-1 Manufacturing management data 

exchange (MANDATE),

Information models

(Property level)

Injection modeling process analysis

CO2PE – unit manufacturing process analysis

3.3.2. Process modeling languages

Process modeling languages have common process modeling elements such as process input, and output, 

and the relationship among them. The IDEF0, BPMN, and PSL are typical process modeling languages. 

Each process modeling language also defines further specialized elements for their own purpose. For 

example, the BPMN classifies processes into “Task” and “Sub process,” and the “Task” element is further 

classified into “Abstract Task,” “Service Task,” “Send Task,” “Receive Task,” and so on. Although 

IDEF0 does not have such specialized processes, it classifies inputs and outputs into four types of data 

flow: inputs, outputs, controls, and mechanisms. PSL is defined in a logical language so that it can define 

logical relationships among activities to represent complex order of precedence and preconditions of 

processes. 

Note that capturing relationships between manufacturing information and sustainability 

performance does not need all of the process modeling elements. The following modeling elements are 

considered as necessary and sufficient elements for unit manufacturing process information modeling. 

• Unit manufacturing processes and operations

• Inputs and outputs

• Operation rules (conditions and restrictions)

• Operation resources 

• Dataflow among operations 
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A unit manufacturing process is a top-level process when developing information models. 

Individual operations within the unit manufacturing process can be considered as sub-levels. For example, 

if an injection molding is a unit manufacturing process, its operations can be: filling plastic pellets, 

clamping a mold, screwing and melting plastic, injecting plastic, cooling a mold, and ejecting a part. Each 

operation requires inputs (i.e., energy and materials) and operation resources (i.e., machines, computers, 

or human) to make outputs (i.e., energy loss, melted plastics, parts). Operation rules control inputs, 

outputs, and operation resources. Dataflow among operations represent inputs and output flow among 

operations. Considering the necessary modeling elements in the unit manufacturing process, the 

IDEF0 or UML can be selected to represent data flow in a unit manufacturing process.

3.3.3. Manufacturing process – activity models

The SIMA (Systems Integration for Manufacturing Applications) activity model is a reference process 

model for product realization. The SIMA activity model is described in IDEF0. The top process in the 

SIMA activity model is “A0: Realize Product,” and it has four sub-processes: “A1: Design Product,” “A2: 

Engineer Manufacture of Product,” “A3: Engineer Production System,” and “A4: Produce Product.” The 

SIMA activity model defines further detail sub-processes as well as inputs, outputs, controls, and 

mechanisms of sub-processes. 

Figure 3 SIMA A0: Realize product activity diagram

The SIMA activity model defines activities where “process model” information is used. One of 

the activities using “process model” information is the “A213: Select Processes” activity. Figure 4 shows 

the activity in IDEF0. 

Realize Products

A0

Product

needs

Market

data

Product orders

Receiving reports

External

Constraints

PRP Model

Design

Constraints

Product

Standards

Products

Facility cost

estimates

Personnel actions

Tolerance

Standards

Evaluation

Guidelines

Manufacturing

Calendar

Production

Requirements

Plant orders

Equipment orders

Tooling/materials

orders

Production

cost estimates

Cost reports

Customer

order status

Product inventory

QFD

methods

Component

catalogs

Materials

knowledge

Design

knowledge

Evaluation

knowledge

Mfg process

knowledge

Resource desc.

Material

stock desc.

Machinability data

Time&cost

ref. data

Process

models

Facility

impl. plans

Design process

knowledge

Facility reports

Planning

Policies

Tooling/materials

Equipment/labor

Sustainability

Constraints
13

Figure 4 A21: Determine manufacturing methods activity in the SIMA model

According to the SIMA model, engineers do the following tasks in the “A213: Select Processes”

activity. 

• Choose the major processes that transform material stocks into the final product, including 

fabrication and assembly processes

• Associate the processes with design or manufacturing features and the related datums and 

tolerances

• Develop the inspection plan

• Determine what features are to be measured or otherwise inspected and what the qualifying 

criteria are

In the A213 activity, engineers make a decision on selecting manufacturing processes to realize a 

product model. Product models, bill of material (BOM), market data, equipment, and material information 

are inputs for the decision. Process models are references for the decision. The inputs can be changed if a 

new product model is introduced, but the references are not. Engineers select the best manufacturing 

process that satisfies time, cost, and resource constraints. Sustainability performance of manufacturing 

processes can be introduced as a new constraint for the process selection activity [32]. 

3.3.4. Manufacturing process - information models

Some manufacturing process standards define information models including manufacturing process class 

definitions and its relationships to other classes. The CMSD standard for example, defines a 

manufacturing process class as shown in Figure 5. The process class in the CMSD defines what 

information is necessary for process planning and simulation. The process class has attributes, which are 

related to other classes, such as produced part, consumed part, required resources, machine program, 

setup and operation time, cost, and material information.
14

Limited Unique Entity

Process

Parts Produced: PartGroup [0..*]

PartsConsumed: PartGroup [0..*]

ResourcesRequired: ResourcesRequired [0..*]

MachineProgramInformation: MachineProgramData [0..1]

SetupTime: Duration [0..1]

OperationTime: Duration [0..1]

RepetetionCount: Integer [0..1]

CostAllocationData: CostAllocationData [0..*]

SpecialInstruction: String [0..1]

ProcessConstraint: ProcessConstraint

IdentifiableEntity

Identifier: Identifier

Description: String [0..1]

ReferenceMaterial: ReferenceMaterialReference [0..*]

Property: Property [0..*]

Figure 5 Process class defined in the CMSD

Although a process class shows what information is related to manufacturing processes, it does 

not tell how the information influences the performance of manufacturing processes. Thiriez [31, 33]

analyzed an injection molding process and developed a process model with property level detail. The 

process model explains how to calculate the amount of necessary energy to produce a kilogram part using 

an injection molding process with given material types and mold shape. An injection modeling process 

consists of several operations, so the total necessary energy of an injection molding is a sum of necessary 

energy for those operations. 

The following equations from reference [31] show two energy models: (EMelt) [Eq. 5 and 6] to 

melt polymers, and (EFill) [Eq.7] to fill a mold with melted polymers. These equations show how 

properties of material and part geometry affect the amount of necessary energy for an injection modeling 

process. 

For non-crystalline polymers: Emelt = mCp(T2 - T1) (5)

For crystalline polymers: Emelt = mCp(T2 - T1) + λmHF (6)

Where, m is the mass

Cp is the specific heat capacity of the polymer

T2 and T1 are the initial temperature and the temperature of the injected polymer

λ is the degree of crystallization, which is a property of material

HF is the heat of fusion for 100 % crystalline polymer

Filling energy

Efill = 2�

1

S+1� 3�

1

S� η0 e−ζ(T−T0) Q

1

S L2 W�1−1

s�

t

�

2

s� (7)

Where, η0 belongs to η = η0 γ

1

S

−1

e−ζ(T −T0)

η is the viscosity of the polymer

γ is the shear rate

W is the width of the plate, and 

L is the length of the plate.
15

One of the goals of the CO2PE! effort is to develop a methodology that allows providing

manufacturing processes related data in a format useful for inclusion in LCI databases. The effort includes 

developing the unit manufacturing process model at the property level. Besides, there are standards like 

the ISO 14048: Environmental management-Life cycle assessment-Data documentation format [34] and 

commercial data formats like the [35] and [36] for the LCI data. The objective was to define data formats 

to exchange LCI data among life cycle assessment (LCA) tools. These data formats are useful to know 

what performance of manufacturing processes needs to be captured in a process model, but they do not

explicitly explain what information influences the performance of manufacturing processes in the 

property level detail. 

Having reviewed the previous manufacturing process models in various abstraction levels, it is 

clear that one generic process model cannot satisfy different usages of the process model. However, we 

believe that reference process models represented as a function between inputs and performance 

indicators in the process selection activity can be very useful (see Figure 6). The function does not tell 

how to calculate performance indicators of a manufacturing process with given inputs. If an individual 

unit manufacturing process had its own process model in property level detail, the process model can be 

used as a reference for process selection activities. 

C = f (P, F,K, M, E)

C : performance indicators of manufacturing processes 

(i.e. throughput, cost, energy and material use, etc.)

f : reference manufacturing model function

P : product models including bills-of-materials, geometry, and tolerance

D : demand

M : selected material information.

R : resources including machines and tools

Figure 6 Function between manufacturing process information and performance

In addition to developing information models, it is important to consider the portability and 

robustness of such information models to implement solutions to support sustainable manufacturing. The 

next section reviews selected software tools that support sustainability analysis. 

3.4. Software Tools

The software tools used for determining sustainability help reduce the time taken for sustainability 

assessment. The tools generally rely on different LCI databases. From the review of the various 

environmental assessment software tools (Table 4), it was observed that measurement of impact 

assessment for a product was based on the LCI database provided by different organizations. The major 

deficiency in these LCI databases is that, details up to the level of individual manufacturing processes are 

not included. These databases provide LCI information, which is based on the BOM. Furthermore, the 

information is region specific and the scientific basis of the LCI is unknown. For example, although LCI 

information is available for cast or rolled steel process, there is no information related to the numerous 

operations being performed on the sheet such as punching, blanking, shearing and bending. Presently 

available software tools, which depend on LCI databases, are therefore incomplete when it comes to 

performance measurement for sustainability. We evaluated several LCA-based software tools [8, 9, 29, 

30-32] and found that manufacturing process-specific LCI is not available.
16

Table 4 Summary of different software tools for sustainability assessment

Software Databases Impact Assessment 

Methods

Major functions

Software 1 USLCI, Ecoinvent, 

GaBi database

CML 96/2001/2007, 

Ecoindicator 95/99, EDIP 

97/2003, TRACI, IO2+ 

and others

Impact 

assessment

Software 2 Ecoinvent, USLCI, 

ELCD, US InputOutput, EU & Danish 

Input & Output, Dutch 

Input –Output, LCA 

Food, Industry Data, 

IVAM, Japanese 

input-output

ReCiPe, Eco-indicator 

99, USEtox, IPCC 2007, 

EPD, Impact 2002+, 

CML-IA, Traci 2, BEES, 

Ecological Footprint 

EDIP 2003, Ecological 

scarcity 2006, EPS 2000, 

Greenhouse

Impact 

assessment

Software 3 No separate database Waste from Electronic 

and Electrical Equipment 

(WEEE) Restriction of 

Hazardous Substances 

(RoHS) 

Impact 

assessment during 

conceptual stage 

of design

Software 4 Cloud hosted Material

Universe -

CO2 footprint, energy 

usage, water usage, and 

RoHS

Selection of 

environmental 

compliance 

material

Other PLM 

Tools

Integrate with internal 

database of the 

company

CO2 footprint, energy 

usage

Environmental 

impact of design 

alternatives

4. Methodology Development

Manufacturing industries need a systems approach to realize sustainability across the enterprise, bridge 

the information gap and deliver business growth [41]. From the above sections it is clear that research and 

development of solution enabling measurement science, methodologies for knowledge management and 

sustainability assessment technologies are crucial to ensure sustainability in manufacturing. 

Manufacturing industries are challenged to remain globally competitive, improve productivity, 

and reduce environmental impacts and energy requirements. These changes require a transformation 

from manufacturing practices based on human experience towards scientific-based modeling, decision 

making, and production. This requires the development of fundamental measurements, standards, and 

knowledge base to enable U.S. manufacturers to make this transformation. The US manufacturing 

industry currently lacks the measurement science to measure and effectively compare the performance of 

unit manufacturing processes with respect to sustainability.

Besides lack of measurement science, manufacturing industries also lack the needed information 

base to measure and effectively compare environmental performance of manufacturing processes, 

resources and services with respect to sustainability. Current use of ad-hoc methods to informally 

describe sustainability of manufacturing processes results in inaccurate and ambiguous comparisons of 

these processes. Furthermore, there are no formal methods for acquiring and exchanging information that 

help establish a consolidated sustainability information base. Information management is crucial for 

sustainable manufacturing through a consolidated information base about the manufacturing processes,

facilities and impact and accessible information models.
17

The subsequent section introduces a sustainability characterization methodology that bridges the 

measurement science and manufacturing knowledge management for sustainable manufacturing. 

4.1. Sustainability Characterization Methodology

The new technical idea is the sustainability characterization through unit manufacturing processes for 

performance modeling and assessment of manufacturing systems. Sustainability characterization will 

create the information crucial in the decision making related to sustainability. Such information includes, 

but not limited to energy, emissions, pollutants, waste and scrap, alternative materials, cycle times and 

productivity. A set of common computable metrics (carbon emissions, material waste, toxicity, etc.) as 

discussed in Section 3.2.2 will be identified that use the sustainability characterized information for 

decisions comparing within and across manufacturing processes for carbon foot printing, energy auditing 

besides others.

The lack of measurement science will be addressed by developing a science-based assessment 

methodology and structured information, based on the fundamental sustainability characterization of unit 

manufacturing processes (UMP). UMPs are those individual operations (e.g., casting, machining, and 

surface treatment) that transform raw material and add value to the work piece as it becomes a final 

product. The measurement science activities for sustainability characterization methodology will 

comprise of (1) definitions of key performance indicators and common computable sustainability 

metrics; (2) formal information model that defines the analytics for computing the manufacturing process 

sustainability; and (3) manufacturing process-specific data sets that instantiate the information models and 

enables execution of computable metrics. Figure 8 illustrates the components of the sustainability 

characterization methodology. Such sustainability characterization will support the required evaluation of 

sustainability performance by allowing science-based rigorous assessment of manufacturing processes.

Key Performance Indicators

Energy and carbon dioxide (CO2)

Waste reduction

Water Usage

Emissions

Hazardous waste

Common Computable Sustainability Metrics

Total energy usage (MWh)

Total gas & oil (MWh)

Total electricity (MWh)

Total CO2 (tonnes)

Total water usage (m3)

Total discharged water (m3)

Total VOC emissions (tonnes)

Total waste-at-cost (tonnes)

Total waste-to-landfill (tonnes)

Manufacturing Processes Information Model

- General process information

- Sustainability specific information

- Best practices

Including Information Acquisition and Usage 

Templates

Manufacturing Processes-Specific Data Sets

General process information

Sustainability specific information

Best practices
18

Figure 8 Sustainability characterization components

To illustrate the usefulness of sustainability characterization, Figures 9 and 10 provide the logical 

model and an example using the injection molding process respectively. One of the challenges is to 

extract the key information of manufacturing processes from all the relevant data available in the form of 

text documents, handbooks, catalogs, etc. Relevant work on the unit process life cycle inventory (uplci) is 

pursued at the Wichita State University. Their uplci profile is for a high production manufacturing 

operation, defined as the use of processes that generally have high automation and are in the medium to 

high throughput production compared to all other machines/equipment that perform a similar operation. 

Figure 9 Logical model for sustainability characterization

As mentioned in Section 3.3.4, a major challenge is to represent the key information as unit 

manufacturing information model, capable of providing the computing functionality for sustainability to 

provide the necessary decision support. Note that this effort can facilitate the development of a structured 

information base by making available a Standard Reference Data for Unit Manufacturing Process 

(SRDUMP) consistent with NIST’s Standard Reference Materials (SRM) [42], and Standard Reference 

Data (SRD) [43] efforts.

Performance 

Indicators

Sustainability

Metrics

Manufacturing Processes

Information Model

Manufacturing Process

Specific Data sets

Computational Model a. Process 1

b. Process 2

c. Process 3

Decision Alternatives

Decision Support

Standard Reference Data Unit Manufacturing Process (SRDUMP)

Relevant data from

Text Documents

Hand Book Data

Catalogs

Online References

Others

Performance 

Indicators

Sustainability

Metrics

IM Process Information 

Model

Injection Molding (IM)

Specific Data sets

Type:

Electric, Hydraulic or Hybrid

Energy Used, Material, 

Waste, etc.

Computational Model

Example: Energy 

a. Hybrid 1 (KWh)

b. Electric 2 (KWh)

c. Hydraulic 3 (KWh)

Decision Alternatives

Decision Support

Standard Reference Data Unit Manufacturing Process (SRDUMP)

IM data from

Text Documents

Hand Book Data

Catalogs

Online References

Others
19

Figure 10 Logical model demonstrating sustainability characterization 

5. Conclusions

This report is a preliminary review of the related and current developments on sustainability for 

manufacturing, with an emphasis on sustainability characterization to facilitate measurement science and 

methodology development to evaluate sustainability performance.

This report first presented how process characterization is an inherent part of performance 

measurement, highlighting the challenges in developing the measurement science for sustainability of 

manufacturing processes. We briefly presented different manufacturing process classifications, 

sustainability indicators, and process information models followed by a brief review of the relevant 

software tools for sustainability. 

To develop a science-based methodology for sustainability characterization, major tasks under 

this project are planned along four phases. Phase one involves the tasks related to the fundamental 

sustainability characterization. Research activities involve requirements gathering, sustainability 

characterization methodology development, and science-based measurement procedures and process data 

representation for UMPs. Phase two involves the tasks aligned with performance modeling of 

manufacturing processes using information generated through sustainability characterization. Research 

activities involve the development of sustainability focused manufacturing process analytical models to 

compute sustainability performance of the UMP. Phase three involves the tasks that lead to the standard 

on sustainability assessment methodology for identified processes and the SRDUMP and phase four

involves the verification and validation of the methodology and standards created.

6. Disclaimer

Certain products or services are identified in the paper to foster understanding. Such identification does 

not imply recommendation or endorsement by the National Institute of Standards and Technology, nor 

does it imply that the products or services identified are necessarily the best available for the purpose. 

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Appendix

Figure A1: Manufacturing Management Taxonomy

 i Taxonomy is a documented and orderly set of types, classifications, categorizations and/or principles that are often achieved through 

mechanisms including but not limited to naming, defining and/or the grouping of attributes, and which ultimately help to describe, differentiate, 

identify, arrange and provide contextual relationships between Manufacturing Items, Entities or Types.

Product Design

Process Design 

& Control

Supply Chain

Management

Product 

Architecture

Producibility

Process

Capacity

Process

Characterization

Operations

Management

Design of

Supply Chain

Logistics &

Coordination

Integral

Modular

Process Availability

Process Capability

Component Standardization

Piece Part Reduction

Tooling Concept

Key Performance Parameters Key Design Characteristics

Availability

Quantity

Rate

Lead Time

Buy

Make

Information Coordination

Physical Distribution

Capacity or Knowledge

Selection Criteria

Contractual Relationship

Key Fabrication 

Characteristics

Process Capability

Process Proofing

Materials Characterization Data

Quality Management Sys.

Environmental Management Sys.

Shop Floor Planning

Subcontractor Management

Tool Control

Preventive Maintenance

Purchasing

Product Data Management

Process Documentation

Inspection

Statistical Control

Non-Destructive Test

Manufacturing Management Taxonomy